Cellular condensates are usually ribonucleoprotein assemblies with liquid- or solid-like properties. Because these subcellular structures lack a delineating membrane, determining their compositions is difficult. Here we describe a proximity-biotinylation approach for capturing the RNAs of the condensates known as processing bodies (PBs) in Arabidopsis (Arabidopsis thaliana). By combining this approach with RNA detection, in silico, and high-resolution imaging approaches, we studied PBs under normal conditions and heat stress. PBs showed a much more dynamic RNA composition than the total transcriptome. RNAs involved in cell wall development and regeneration, plant hormonal signaling, secondary metabolism/defense, and RNA metabolism were enriched in PBs. RNA-binding proteins and the liquidity of PBs modulated RNA recruitment, while RNAs were frequently recruited together with their encoded proteins. In PBs, RNAs follow distinct fates: in small liquid-like PBs, RNAs get degraded while in more solid-like larger ones, they are stored. PB properties can be regulated by the actin-polymerizing SCAR (suppressor of the cyclic AMP)-WAVE (WASP family verprolin homologous) complex. SCAR/WAVE modulates the shuttling of RNAs between PBs and the translational machinery, thereby adjusting ethylene signaling. In summary, we provide an approach to identify RNAs in condensates that allowed us to reveal a mechanism for regulating RNA fate.

Pathway analysis methodologies couple traditional gene expression analysis with knowledge encoded in established molecular pathway networks, offering a promising approach towards the biological interpretation of phenotype differentiating genes. Early pathway analysis methodologies, named as gene set analysis (GSA), view pathways just as plain lists of genes without taking into account either the underlying pathway network topology or the involved gene regulatory relations. These approaches, even if they achieve computational efficiency and simplicity, consider pathways that involve the same genes as equivalent in terms of their gene enrichment characteristics. Most recent pathway analysis approaches take into account the underlying gene regulatory relations by examining their consistency with gene expression profiles and computing a score for each profile. Even with this approach, assessing and scoring single-relations limits the ability to reveal key gene regulation mechanisms hidden in longer pathway sub-paths. We introduce MinePath, a pathway analysis methodology that addresses and overcomes the aforementioned problems. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation sub-paths. Regulation sub-paths are then matched with gene expression sample profiles in order to evaluate their functional status and to assess phenotype differential power. Assessment of differential power supports the identification of the most discriminant profiles. In addition, MinePath assess the significance of the pathways as a whole, ranking them by their p-values. Comparison results with state-of-the-art pathway analysis systems are indicative for the soundness and reliability of the MinePath approach. In contrast with many pathway analysis tools, MinePath is a web-based system (www.minepath.org) offering dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination. This unique characteristic makes MinePath a valuable tool for in silico molecular biology experimentation as it serves the biomedical researchers’ exploratory needs to reveal and interpret the regulatory mechanisms that underlie and putatively govern the expression of target phenotypes. It is generally recognized that using different sources of information and knowledge is better than just using a single source. This is most profound in the post-genomics era. On one hand, the advent of genomic high-throughput technologies realized by DNA microarray and next generation RNAseq technologies enabled a ‘systems level analyses’ by offering the ability to measure the expression status of thousands of genes in parallel. On the other, molecular pathway networks depict the interaction of DNA segments during the transcription of genes into mRNA. The prominent and vital role of pathways in the study of various biology processes is a major sector in contemporary biology research. We introduce MinePath, a pathway analysis methodology that amalgamates information and knowledge from gene expression profiles and molecular pathways. The novelty of MinePath resides in its ability to target not just the genes involved in the pathways, as most of existing methodologies and tools do, but directly their interrelations and interactions. With this approach, the regulatory machinery that putatively governs and guides the expression of disease phenotypes can be explored and revealed.

The high genetic diversity of HLA across populations significantly confines the effectiveness of a donor or umbilical cord blood search for allogeneic hematopoietic stem cell transplantation (HSCT). This study aims to probe the HLA immunogenetic profile of the population of Crete, a Greek region with specific geographic and historical characteristics, and to investigate potential patterns in HLA distribution following comparison with the Deutsche Knochenmarkspenderdatei (DKMS) donor registry. It also aims to highlight the importance of regional public cord blood banks (PCBBs) in fulfilling HSCT needs, especially in countries with significant genetic diversity. A cohort of 1835 samples representative of the Cretan population was typed for HLA class I (HLA-A, HLA-B, HLA-C) and class II (HLA-DRB1, HLA-DQB1, HLA-DPB1) loci by high-resolution second field next-generation sequencing. Data were compared with the respective HLA profiles of 12 DKMS populations (n = 20 032). Advanced statistical and bioinformatics methods were employed to assess specific intra- and inter-population genetic indexes associated with the regional and geographic distribution of HLA alleles and haplotypes. A considerable HLA allelic and haplotypic diversity was identified among the Cretan samples and between the latter and the pooled DKMS cohort. Even though the HLA allele and haplotype frequency distribution was similar to regions of close geographic proximity to Crete, a clinal distribution pattern from the northern to southern regions was identified. Significant differences were also observed between Crete and the Greek population of DKMS. This study provides an in-depth characterization of the HLA immunogenetic profile in Crete and reveals the importance of demographic history in HLA heterogeneity and donor selection. The novel HLA allele and haplotype frequency comparative data between the Cretan and other European populations signify the importance of regional PCBBs in prioritizing HLA diversity to efficiently promote the HSCT program at the national level and beyond.